Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications

Sensors
Jian-Lei KongJian-Li Wang

Abstract

In this paper, a novel semi-supervised segmentation framework based on a spot-divergence supervoxelization of multi-sensor fusion data is proposed for autonomous forest machine (AFMs) applications in complex environments. Given the multi-sensor measuring system, our framework addresses three successive steps: firstly, the relationship of multi-sensor coordinates is jointly calibrated to form higher-dimensional fusion data. Then, spot-divergence supervoxels representing the size-change property are given to produce feature vectors covering comprehensive information of multi-sensors at a time. Finally, the Gaussian density peak clustering is proposed to segment supervoxels into sematic objects in the semi-supervised way, which non-requires parameters preset in manual. It is demonstrated that the proposed framework achieves a balancing act both for supervoxel generation and sematic segmentation. Comparative experiments show that the well performance of segmenting various objects in terms of segmentation accuracy (F-score up to 95.6%) and operation time, which would improve intelligent capability of AFMs.

References

May 30, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Radhakrishna AchantaSabine Süsstrunk
Jun 28, 2014·Science·Alex Rodriguez, Alessandro Laio

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Citations

Apr 28, 2019·Sensors·Klemen KendaDunja Mladenić

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Methods Mentioned

BETA
AFM
feature extraction

Software Mentioned

Point Cloud Library ( PCL )
3D
CIELAB
SEED
Ubuntu

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